Advances In Computer Science Research Articles

Towards Digital Twinning on the Web: Heterogeneous 3D Data Fusion Based on Open-Source Structure

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

Machine Learning for Precision Epilepsy Surgery.

Brain surgery offers the best chance of seizure-freedom for patients with focal drug-resistant epilepsy, but only 50% achieve sustained seizure-freedom. With the explosion of data collected during routine presurgical evaluations and recent advances in computational science, we now have a tremendous potential to achieve precision epilepsy surgery: a data-driven tailoring of surgical planning. This review highlights the clinical need, the relevant computational science focusing on machine learning, and discusses some specific applications in epilepsy surgery.

ADVANCED CLUSTERING OF ARCHITECTURAL GEOMETRIC ORNAMENTS USING SMALL SCALE MACHINE LEARNING, CASE STUDY OF ILKHANID GEOMETRIC PATTERNS

Abstract. Classification is an essential step for architectural historians for better understanding and typology of cultural heritage. This research aims to automatically cluster geometric ornaments of the Ilkhanid period in Iran through using machine learning It examines the application of advanced computer science tools and methods in analysis of architectural heritage by searching the possibility of clustering ornament images with machine learning into different clusters. After examining case studies of mosques, tombs or other buildings in Iran from the Ilkhanid period (1256–1335 CE), 231 images from 36 existing buildings were chosen, and after editing images, an inventory was created based on characterization of each ornament (image) containing: Buildings’ Name, Region, Construction Date, Functionality, Repetition of Motifs, Material, Ornamental Types, Design Complexity, Dominant Colour, Star-Number, Geometric Shape, Geometric Lines, and Geometric Pieces. Next, these images were analysed with small-scale machine learning with the help of the visual programming toolbox Orange (http://orange.biolab.si). The results containing image groups (machine clustered images) were tested with the characterization table of ornaments, and groups of ornaments that represents a style is introduced.

Recent Advancements in Computer Science: A Comprehensive Review of Emerging Technologies and Innovations

Recent advances in computer science have led to many new technologies and inventions that continue to affect our environment. This extensive study highlights some of the most significant computer science discoveries and their possible applications. This assessment begins with AI, which has driven many recent achievements. Cutting-edge machine learning methods, deep neural networks, reinforcement learning, and explainable AI are discussed. AI and adjacent sciences like natural language processing (NLP) and computer vision have led to innovative applications in healthcare, finance, autonomous cars, and smart cities. The second part discusses quantum computing, which promises exponentially increased processing power. Qubits were created by studying quantum physics concepts like superposition and entanglement. These qubits enable quantum computers to tackle complicated problems that traditional computers cannot. The review discusses quantum hardware, error correction, and algorithms and their potential applications. Next, blockchain technology, which has changed data management and security, is examined. Cryptocurrencies and DApps and smart contracts have grown due to blockchain's decentralisation and tamper-resistance. This section discusses new consensus techniques, scalability, and blockchain's use in supply chain management and digital identification. In this study, the Internet of Things (IoT) shows how linked gadgets have changed businesses and customer experiences. IoT, cloud, and edge computing have established an environment for real-time data processing, predictive analytics, and autonomous decision-making. Privacy, security, and standards issues are discussed. Given the growing dangers from malevolent actors in a digitalized environment, the evaluation finishes with cybersecurity advances. This section discusses improved encryption, AI-driven cybersecurity, and quantum-resistant cryptography in light of quantum computing's ability to break standard cryptographic algorithms.

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics.

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.

Recent Advancements in Agriculture Robots: Benefits and Challenges

In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

A FRAMEWORK TO INTEGRATE BIM WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING-BASED PROPERTY VALUATION METHODS

Abstract. Property valuation is of extreme importance since variations in the real estate market enormously influence people’s life. The main goal of Automated Valuation Models (AVMs) is to calculate the market value of a large number of properties with an acceptable accuracy. The Hedonic Price Model (HPM) is the most widely used AVM for the valuation purposes. Despite its simplicity, ease of use and straightforwardness, HPM lacks the capability to address the non-linear relationships between different value-related factors. Hence, researchers have developed other state-of-the-art property valuation methods based on the advancements in computer science including Artificial Intelligence (AI), Machine Learning (ML), computer vision and deep learning. Design, development, and validation of such advanced AVMs require establishment of a database including data on the different influential factors. Two types of factors are used in the literature, including textual and visual features. Reliable data sources are required for the implementation of AVMs since the accuracy of the provided valuations is definitely linked to the reliability of the used real estate databases. Building Information Modelling (BIM) provides precise information on different components of properties. Although some scholars have tried to use BIM for property valuation, BIM benefits in different valuation procedures have not been fully investigated. Hence, this paper provides a framework that consider BIM capabilities to be integrated with different stages and processes in property valuation, especially in relation to advanced AVMs based on AI and ML.

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How to change a state: Broadening participation in K-12 computer science education

Computer science education has been making dramatic increases in recent years. Across the US, different states are advancing computer science education through different policies. However, as a state makes choices to advance computer science education, it is critical to consider how these policies will broaden participation in computing (BPC). Many have indicated that only white and Asian males (who make up 30% of our population) currently have the opportunity/privilege to engage in computer science education. Therefore, as we implement state-level computer science education reform, it is critical that BPC remains as our guiding principle. Expanding Computing Education Pathways (ECEP) was created as an NSF national alliance to support state-level educational reform with regards to computer science. Over the past 6 years, this alliance of 22 states and Puerto Rico have worked together to share policies to advance BPC in each state. Through these experiences, ECEP has proposed that state change related to CS educational reform follows five stages: (1) Find your leader(s) and change agents; (2) understand the CS education landscape and identify the key issues/policies; (3) gather and organize your allies to establish goals and develop strategic plans and; (4) get initial funding to support change and; (5) building and utilizing data infrastructure that informs strategic BPC efforts. This study examined the ECEP alliance and the five-stage model through the 25,000+ documents and data sources over the past decade, specifically investigating how these five stages impacted states’ overall BPC efforts. Results indicated that these 5 stages seemed to support states’ BPC efforts.

Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak.

Forecasting the stock market is one of the most difficult undertakings in the financial industry due to its complex, volatile, noisy, and nonparametric character. However, as computer science advances, an intelligent model can help investors and analysts minimize investment risk. Public opinion on social media and other online portals is an important factor in stock market predictions. The COVID-19 pandemic stimulates online activities since individuals are compelled to remain at home, bringing about a massive quantity of public opinion and emotion. This research focuses on stock market movement prediction with public sentiments using the long short-term memory network (LSTM) during the COVID-19 flare-up. Here, seven different sentiment analysis tools, VADER, logistic regression, Loughran–McDonald, Henry, TextBlob, Linear SVC, and Stanford, are used for sentiment analysis on web scraped data from four online sources: stock-related articles headlines, tweets, financial news from "Economic Times" and Facebook comments. Predictions are made utilizing both feeling scores and authentic stock information for every one of the 28 opinion measures processed. An accuracy of 98.11% is achieved by using linear SVC to calculate sentiment ratings from Facebook comments. Thereafter, the four estimated sentiment scores from each of the seven instruments are integrated with stock data in a step-by-step fashion to determine the overall influence on the stock market. When all four sentiment scores are paired with stock data, the forecast accuracy for five out of seven tools is at its most noteworthy, with linear SVC computed scores assisting stock data to arrive at its most elevated accuracy of 98.32%.

An Improved Oil Painting Formation Using Advanced Image Processing

The fast advancement of computer science and information technology has resulted in a significant revolution in the commercial painting sector. The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. With the rapid development and comprehensive popularization of computer technology, more and more people use computer image processing technology in oil painting created as a new way of creation. Some artists use a variety of ways to study the way of oil painting creation and use image processing technology to stimulate the creator’s imagination and improve the efficiency of drawing sketches in optimizing creative ideas, drawing, and processing original works. However, the value of art comes from creation. Oil painting creation based on computer image processing is a new type of artistic production that still requires the integration of different artists’ ideas and creative concepts. The image processing technology is a tool and a way for artists to make art while also realizing the convergence of science, technology, and art. This paper uses computer image processing technology to study oil painting creation. First, it briefly discusses computer image processing technology, focusing on picture preprocessing and image graying, and then uses this technique in formation of oil painting. Second, it establishes the oil painting mathematical, direction field, and color conversion models completing the oil painting image creation by using this model. Finally, the advantages of this technology are analyzed from the two aspects of oil painting creation, painting technique, and brush direction. The results show that 75% of artists are very satisfied with the digital painting technique, and 65% of artists are very satisfied with the painting brush direction, which shows that the application effect of computer image processing technology in oil painting creation is remarkable.

A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives

Intelligent mobile robots that can move independently were laid out in the real world around 100 years ago during the second world war after advancements in computer science. Since then, mobile robot research has transformed robotics and information engineering. For example, robots were crucial in military applications, especially in teleoperations, when they emerged during the second world war era. Furthermore, after the implementation of artificial intelligence (AI) in robotics, they became autonomous or more intelligent. Currently, mobile robots have been implemented in many applications like defense, security, freight, pattern recognition, medical treatment, mail delivery, infrastructure inspection and developments, passenger travel, and many more because they are more intelligent nowadays with artificial intelligence technology. To study the developments of mobile robots, we have studied an extensive literature survey of the last 50 years. In this article, we discuss a complete century of mobile robotics research, major sensors used in robotics, some major applications of mobile robots, and their impact on our lives and in applied engineering.

Information retrieving competence of the students during COVID-19

With the advancement of computer science and information technology, enormous generation of data and information in particular has been observed in every field of knowledge over the past decades. The ultimate purpose of information creation is to serve the need of information seekers. Due to complex nature of information, it needs to be organised and retrieved effectively so that it can be put to use. Information Retrieval can be defined as the process of representing, storing, organising and accessing of information, generally documents that are suitable to an information need at the user end. The present study attempts to make an analysis of information retrieving competence of the students of the University of Jammu during COVID-19. In order to meet the purpose of this study, survey method was adopted and a questionnaire was distributed online to the students. The outcomes of the study reveal if information retrieval was difficult during the period, various reasons for information seeking and the effect on information retrieving ability of the students. It also brings into light the capability of students to evaluate information for further use and the things they have learnt while retrieving information during the pandemic.

Extension of Critical Programs of the Computational Theory of Mind

Technological advances in computer science have secured the computer metaphor status of a heuristic methodological tool used to answer the question about the nature of mind. Nevertheless, some philosophers strongly support opposite opinions. Anti-computationalism in the philosophy of mind is a methodological program that uses extremely heterogeneous grounds for argumentation, deserving analysis and discussion. This article provides an overview and interpretation of the traditional criticism of the computational theory of mind (computationalism); its basic theses have been formed in Western philosophy in the last quarter of the 20th century. The main goal is to reveal the content of the arguments of typical anti-computationalist programs and expand their application to the framework of the semantic problems of the Classic Computational Theory of Mind. The main fault of the symbolic ap-proach in the classical computationalism is the absence of a full-fledged theory of semantic properties. The relevance of considering these seemingly outdated problems is justified by the fact that the problem of meaning (and general problems of semantics) remains in the core of the latest developments in various areas of AI and the principles of human-computer interaction.

A CPN-Based Approach for Studying Impacts of Communication Delays on Safety and Availability of Safety-Critical Distributed Networked Control Systems

With the great advances in computer science and communication technology, more and more control systems are implemented as distributed networked control systems (DNCSs). Due to the nature of the time delay of communication networks, it is of importance to investigate how communication delays affect the systems from different perspectives. Most of the literature by far focus on analyzing the impacts of time delays on the system stability or safety control with mathematical models (i.e., differential equations), which are of interest in the early phases of the system development (e.g., the conceptual phase). However, in the later phases of the system development (e.g., architecture design or system implementation), qualitative as well as quantitative safety analysis based on system models that describe the concrete structures, interactions between components, and state transitions of the underlying systems is desirable. Additionally, the availability of a control system is of interest from the perspective of operation. This article studies the impacts of communication delays on the safety and availability of DNCSs by a colored-Petri-net-based approach. To exemplify the proposed approach, a simplified communication-based train control system is presented.

An Online Predictive System for Mapping Visual Scenes of Fraudulent Behaviour

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

A Comparative Study of LSB based Statistical Steganalysis and Gray Level Co-Occurrence Matrix based Blind Image Steganalysis

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Feature Selection and Ensemble Method Analysis for Breast Cancer Datasets

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

A Review on Comparison of Human Bite Marks in Forensic Images

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Llaimas Volcano Seismic Event Classification Using The Cross-Correlation Function

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.